Equity

Market-Dependent Communication in Multi-Agent Alpha Generation ArXiv ID: 2511.13614 “View on arXiv” Authors: Jerick Shi, Burton Hollifield Abstract Multi-strategy hedge funds face a fundamental organizational choice: should analysts generating trading strategies communicate, and if so, how? We investigate this using 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing five organizational structures from isolated baseline to collaborative and competitive conversation. We show that communication improves performance, but optimal communication design depends on market characteristics. Competitive conversation excels in volatile technology stocks, while collaborative conversation dominates stable general stocks. Finance stocks resist all communication interventions. Surprisingly, all structures, including isolated agents, converge to similar strategy alignments, challenging assumptions that transparency causes harmful diversity loss. Performance differences stem from behavioral mechanisms: competitive agents focus on stock-level allocation while collaborative agents develop technical frameworks. Conversation quality scores show zero correlation with returns. These findings demonstrate that optimal communication design must match market volatility characteristics, and sophisticated discussions don’t guarantee better performance. ...

Discovery of a 13-Sharpe OOS Factor: Drift Regimes Unlock Hidden Cross-Sectional Predictability ArXiv ID: 2511.12490 “View on arXiv” Authors: Mainak Singha Abstract We document a high-performing cross-sectional equity factor that achieves out-of-sample Sharpe ratios above 13 through regime-conditional signal activation. The strategy combines value and short-term reversal signals only during stock-specific drift regimes, defined as periods when individual stocks show more than 60 percent positive days in trailing 63-day windows. Under these conditions, the factor delivers annualized returns of 158.6 percent with 12.0 percent volatility and a maximum drawdown of minus 11.9 percent. Using rigorous walk-forward validation across 20 years of S&P 500 data (2004 to 2024), we show performance roughly 13 times stronger than market benchmarks on a risk-adjusted basis, produced entirely out-of-sample with frozen parameters. The factor passes extensive robustness tests, including 1,000 randomization trials with p-values below 0.001, and maintains Sharpe ratios above 7 even under 30 percent parameter perturbations. Exposure to standard risk factors is negligible, with total R-squared values below 3 percent. We provide mechanistic evidence that drift regimes reshape market microstructure by amplifying behavioral biases, altering liquidity patterns, and creating conditions where cross-sectional price discovery becomes systematically exploitable. Conservative capacity estimates indicate deployable capital of 100 to 500 million dollars before noticeable performance degradation. ...

Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China’s A-Share Market ArXiv ID: 2510.21147 “View on arXiv” Authors: Chujun He, Zhonghao Huang, Xiangguo Li, Ye Luo, Kewei Ma, Yuxuan Xiong, Xiaowei Zhang, Mingyang Zhao Abstract We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents – Fundamental, Technical, Report, and News – that conduct in-depth analyses of individual firms to ensure both breadth and depth of coverage; (iii) a Portfolio agent that uses reinforcement learning to combine the agent outputs into a unified policy to generate the trading strategy; and (iv) a Risk Control agent that adjusts portfolio positions in response to market volatility. We evaluate the system on the constituents by the CSI 300 Index of China’s A-share market and find that it consistently outperforms standard benchmarks and a state-of-the-art multi-agent trading system on risk-adjusted returns and drawdown control. Our core contribution is a hierarchical multi-agent design that links top-down macro screening with bottom-up fundamental analysis, offering a robust and extensible approach to factor-based portfolio construction. ...

FinCARE: Financial Causal Analysis with Reasoning and Evidence ArXiv ID: 2510.20221 “View on arXiv” Authors: Alejandro Michel, Abhinav Arun, Bhaskarjit Sarmah, Stefano Pasquali Abstract Portfolio managers rely on correlation-based analysis and heuristic methods that fail to capture true causal relationships driving performance. We present a hybrid framework that integrates statistical causal discovery algorithms with domain knowledge from two complementary sources: a financial knowledge graph extracted from SEC 10-K filings and large language model reasoning. Our approach systematically enhances three representative causal discovery paradigms, constraint-based (PC), score-based (GES), and continuous optimization (NOTEARS), by encoding knowledge graph constraints algorithmically and leveraging LLM conceptual reasoning for hypothesis generation. Evaluated on a synthetic financial dataset of 500 firms across 18 variables, our KG+LLM-enhanced methods demonstrate consistent improvements across all three algorithms: PC (F1: 0.622 vs. 0.459 baseline, +36%), GES (F1: 0.735 vs. 0.367, +100%), and NOTEARS (F1: 0.759 vs. 0.163, +366%). The framework enables reliable scenario analysis with mean absolute error of 0.003610 for counterfactual predictions and perfect directional accuracy for intervention effects. It also addresses critical limitations of existing methods by grounding statistical discoveries in financial domain expertise while maintaining empirical validation, providing portfolio managers with the causal foundation necessary for proactive risk management and strategic decision-making in dynamic market environments. ...

Fusing Narrative Semantics for Financial Volatility Forecasting ArXiv ID: 2510.20699 “View on arXiv” Authors: Yaxuan Kong, Yoontae Hwang, Marcus Kaiser, Chris Vryonides, Roel Oomen, Stefan Zohren Abstract We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain: (i) aligning and fusing heterogeneous data modalities, numerical financial data and textual information, and (ii) mitigating look-ahead bias that can undermine the validity of financial models. To achieve this, M2VN combines open-source market features with news embeddings generated by Time Machine GPT, a recently introduced point-in-time LLM, ensuring temporal integrity. An auxiliary alignment loss is introduced to enhance the integration of structured and unstructured data within the deep learning architecture. Extensive experiments demonstrate that M2VN consistently outperforms existing baselines, underscoring its practical value for risk management and financial decision-making in dynamic markets. ...

Market-Implied Sustainability: Insights from Funds’ Portfolio Holdings ArXiv ID: 2510.20434 “View on arXiv” Authors: Rosella Giacometti, Gabriele Torri, Marco Bonomelli, Davide Lauria Abstract In this work, we aim to develop a market-implied sustainability score for companies, based on the extent to which a stock is over- or under-represented in sustainable funds compared to traditional ones. To identify sustainable funds, we rely on the Sustainable Finance Disclosure Regulation (SFDR), a European framework designed to clearly categorize investment funds into different classes according to their commitment to sustainability. In our analysis, we classify as sustainable those funds categorized as Article 9 - also known as “dark green” - and compare them to funds categorized as Article 8 or Article 6. We compute an SFDR Market-Implied Sustainability (SMIS) score for a large set of European companies. We then conduct an econometric analysis to identify the factors influencing SMIS and compare them with state-of-the-art ESG (Environmental, Social, and Governance) scores provided by Refinitiv. Finally, we assess the realized risk-adjusted performance of stocks using portfolio-tilting strategies. Our results show that SMIS scores deviate substantially from traditional ESG scores and that, over the period 2010-2023, companies with high SMIS have been associated with significant financial outperformance. ...

Aligning Multilingual News for Stock Return Prediction ArXiv ID: 2510.19203 “View on arXiv” Authors: Yuntao Wu, Lynn Tao, Ing-Haw Cheng, Charles Martineau, Yoshio Nozawa, John Hull, Andreas Veneris Abstract News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140,000 pairs of Bloomberg English and Japanese news articles covering around 3500 stocks in Tokyo exchange over 2012-2024. Aligned sentences are sparser, more interpretable, and exhibit higher semantic similarity. Return scores constructed from aligned sentences show stronger correlations with realized stock returns, and long-short trading strategies based on these alignments achieve 10% higher Sharpe ratios than analyzing the full text sample. ...

An Empirical study on Mutual fund factor-risk-shifting and its intensity on Indian Equity Mutual funds ArXiv ID: 2510.19619 “View on arXiv” Authors: Rajesh ADJ Jeyaprakash, Senthil Arasu Balasubramanian, Vijay Maddikera Abstract Investment style groups investment approaches to predict portfolio return variations. This study examines the relationship between investment style, style consistency, and risk-adjusted returns of Indian equity mutual funds. The methodology involves estimating size and style beta coefficients, identifying breakpoints, analysing investment styles, and assessing risk-shifting intensity. Funds transition across styles over time, reflecting rotation, drift, or strengthening trends. Many Mid Blend funds remain in the same category, while others shift to Large Blend or Mid Value, indicating value-oriented strategies or large-cap exposure. Some funds adopt high-return styles like Small Value and Small Blend, aiming for alpha through small-cap equities. Performance changes following risk structure shifts are analyzed by comparing pre- and post-shift metrics, showing that style adjustments can enhance returns based on market conditions. This study contributes to mutual fund evaluation literature by highlighting the impact of style transitions on returns. ...

AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration ArXiv ID: 2509.25055 “View on arXiv” Authors: Binqi Chen, Hongjun Ding, Ning Shen, Jinsheng Huang, Taian Guo, Luchen Liu, Ming Zhang Abstract The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for generating formulaic alphas, existing frameworks are fundamentally hampered by a triad of interconnected issues. First, they suffer from reward sparsity, where meaningful feedback is only available upon the completion of a full formula, leading to inefficient and unstable exploration. Second, they rely on semantically inadequate sequential representations of mathematical expressions, failing to capture the structure that determine an alpha’s behavior. Third, the standard RL objective of maximizing expected returns inherently drives policies towards a single optimal mode, directly contradicting the practical need for a diverse portfolio of non-correlated alphas. To overcome these challenges, we introduce AlphaSAGE (Structure-Aware Alpha Mining via Generative Flow Networks for Robust Exploration), a novel framework is built upon three cornerstone innovations: (1) a structure-aware encoder based on Relational Graph Convolutional Network (RGCN); (2) a new framework with Generative Flow Networks (GFlowNets); and (3) a dense, multi-faceted reward structure. Empirical results demonstrate that AlphaSAGE outperforms existing baselines in mining a more diverse, novel, and highly predictive portfolio of alphas, thereby proposing a new paradigm for automated alpha mining. Our code is available at https://github.com/BerkinChen/AlphaSAGE. ...

Exponential Hedging for the Ornstein-Uhlenbeck Process in the Presence of Linear Price Impact ArXiv ID: 2509.25472 “View on arXiv” Authors: Yan Dolinsky Abstract In this work we study a continuous time exponential utility maximization problem in the presence of a linear temporary price impact. More precisely, for the case where the risky asset is given by the Ornstein-Uhlenbeck diffusion process we compute the optimal portfolio strategy and the corresponding value. Our method of solution relies on duality, and it is purely probabilistic. ...